The present disclosure provides for a system and method for multifunction segmented array compensation (“M-SAC”) that can be applied to compensate electronic oscillators for one or more environmental parameters. As discussed herein the M-SAC system and method provide for a novel solution that overcomes the limitations of the prior art.
While the use of an artificial neural network (“ANN”) provides for a good curve fit and is widely known, this approach has several significant weaknesses including a slow solution speed and unreliable results. For example, the ANN methodology requires several minutes for frequency versus temperature performance and tens of minutes to an half hour or more for trim effect compensation. The results obtained using the ANN methodology are also unpredictable, requiring a user to repeat the solutions several times before obtaining a workable result. The need to repeat solutions leads to even longer solution times, on the order of tens of minutes for frequency versus temperature and over an hour for trim effect compensation. The slow and unreliable nature of the ANN methodology significantly limits throughput and manufacturability.
Other limitations of the prior art include the lack of enabling a user-defined performance level. For example, thermistor resistor networks provide for fitting a solution based on discrete values for portions of test data and then finding the best solution for those portions. For example, if a user desired a performance of 250 ppb, but the solution resulted in 750 ppb, the user has no mechanism for correcting the performance. Polynomial generators fit the curve with a single polynomial for a fixed order, typically 3rd or 5rd order polynomials. A user is limited to using only the best fit for these polynomials. Microcontroller Compensated Crystal Oscillators (“MCXO”) use algorithms at a fixed interval of points and linear interpolation. Drawing straight lines between points means that data can never be fit better than a straight line segment. Using ANN, a user is restricted to the training of the ANN and the number of neurons given to the solution. Adding more and more neurons to a solution in an attempt to achieve the desired performance level results in a more complicated network, makes the solution harder to solve, and increases the likelihood that the algorithm will be stuck in local minima.
As can be seen from this review of the prior art, there exists a need for a commercially viable solution to compensate oscillators for environmental parameters that is fast, reliable, and efficient. It would also be advantageous if such as system and method enabled a user to define and customize the necessary performance of the solution for a given application.